Automatic Speech Recognition for the Basa`a Language: A Low-Resource Approach
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Abstract (arXiv)
The rapid advancement of Artificial Intelligence (AI) and Natural Language Processing (NLP) has revolutionized the way humans interact with machines. Among the most impactful developments is Automatic Speech Recognition (ASR), which enables computers to convert spoken language into text. Systems such as those built on deep neural networks, transformer architectures, and self-supervised learning have achieved near-human performance for well-resourced languages such as English and French. Yet, these advances have disproportionately benefited a small fraction of the world's languages.
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References · 5
- 2017Attention Is All You Needcited by 60,361
- 2020wav2vec 2.0: A Framework for Self-Supervised Learning of Speech Representationscited by 3,905
- 2021XLS-R: Self-supervised Cross-lingual Speech Representation Learning at Scalecited by 601
- 2025Charting the Landscape of African NLP: Mapping Progress and Shaping the Road Aheadcited by 16
- 2022Tone prediction and orthographic conversion for Basaacited by 1
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